Enhancing Well Log Data with Artificial Neural Networks: Insights into Carbonate Reservoirs of the Azkand Formation, Khabbaz Oil Field, Northern Iraq

Hussein S. Hussein, Department of Petroleum Technology, Erbil Technology College, Erbil Polytechnic University, Erbil 44002, Kurdistan Region, IraqFollow

Article Type

Research Paper

Corresponding Author

Hussein S. Hussein

Highlights

Artificial neural networks (ANNs) effectively enhanced porosity and permeability predictions from well logs.
The ANN model achieved high accuracy with R² = 0.93 for porosity and R² = 0.80 for permeability.
Fracture identification and secondary porosity analysis revealed diagenetic enhancement of reservoir quality.
Integrating ANN with conventional logs provides a robust workflow for complex carbonate reservoir characterization.

Abstract

Accurate reservoir characterization is critical for optimizing hydrocarbon exploration and production, particularly in heterogeneous carbonate reservoirs. This study focuses on the Azkand Formation within the tectonically complex Zagros Fold Belt, employing a multidisciplinary approach to bridge knowledge gaps in petrophysical analysis. By integrating conventional well log data with artificial neural networks (ANNs), we enhanced the prediction of porosity and permeability, calibrated against core measurements. Fractures, critical to fluid flow in low-porosity carbonate succession, were identified using caliper, sonic, neutron, and density logs. Lithology and shale content were determined using neutron-density crossplot and gamma-ray data, respectively, while gas zones were detected via neutron-density crossover analysis. The ANN model demonstrated a high coefficient of determination (R² = 0.93) for porosity predictions and a good correlation (R² = 0.8) for permeability predictions. Additionally, secondary porosity was identified as a dominant feature, resulting from diagenetic processes such as fracturing. This comprehensive workflow highlights the potential of advanced computational techniques combined with traditional methods to characterize complex reservoirs like the Azkand Formation effectively.

Keywords

Azkand Formation; Artificial Neural Networks (ANNs); Reservoir Characterization; Carbonate Reservoirs; Fracture Analysis; Khabbaz Oil Field

Recommended Citation

Hussein, Hussein S. (2025) "Enhancing Well Log Data with Artificial Neural Networks: Insights into Carbonate Reservoirs of the Azkand Formation, Khabbaz Oil Field, Northern Iraq," Egyptian Journal of Petroleum: Vol. 35 : Iss. 1 , Article 2.
Available at: https://doi.org/10.62593/2090-2468.1091